Leaf area index (LAI) is an important parameter for monitoring crop growth, and an important input parameter for crop yield prediction model, hydrological and climatic models. LAI can be used in field crop growth monitoring and verification of remote sensing products. Therefore, accurate, rapid and large-scale estimation of LAI is not only conducive to better monitoring crops, but also conducive to its application in modeling, crop management and precision agriculture. Remote sensing technique has become a promising method to detect and monitor the crop LAI due to its many advantages. In this paper, Banjiequan Village, Wumachang Township, Qitai County, Xinjiang, China was selected as the study area. In order to rapidly and extensively retrieve LAI of winter wheat using domestic remote sensing images, 17 common vegetation indices were extracted from the GF-1/2 images, which was synchronized with field sampling, and observation at intervals of 20 m in the east-west direction and 30 m in the north-south direction in a 130 m × 420 m block. A total of 78 sampling points were taken from a small area of 4 rows × 50 cm. Sampling width was measured by tape ruler and coordinates of sampling points were given by GPS. Based on the vegetation indices extracted from GF-1/2 image and LAI data measured at erecting stage, jointing stage and flowering stage, we established univariate (linear, exponential, power, quadratic polynomials) and multivariate (partial least squares regression, PLSR) empirical models for inversion of winter wheat LAI, and validated them. The correlation coefficients of LAI with MSR (modified simple ratio), GNDVI (green normalized difference vegetation index), EVI (enhanced vegetation index) extracted from the erecting, jointing and flowering stages of GF-1 were the maximum, which were 0.708, 0.671, and 0.743, respectively, indicating that the correlation between these vegetation indices and LAI of winter wheat was significant. The univariate model R 2 based on MSRGF-1, NDVIGF-2 (normalized difference vegetation index), GNDVIGF-1 at jointing stage and EVIGF-1 at flowering stage were all greater than 0.7. Compared with different image data at the same growth stage, the quadratic polynomial model based on NDVIGF-2 and PLSR model based on NDVIGF-2, MSRGF-2, and SAVIGF-2 (soil-adjusted vegetation index) were more precise than those based on GF-1, with R 2 of 0.768 and 0.809 respectively. Compared with the models with the same data (GF-1) at different growth stages, the quadratic polynomial model based on GNDVIGF-1 at jointing stage and the PLSR model based on EVIGF-1, GSRGF-1 (green simple ratio) and NDVIGF-1 at flowering stage had the maximum value of R 2, which was 0.783. The RMSE of the PLSR model was smaller than that of the quadratic polynomial model, indicating the stability of the multivariate model was better than univariate model. Analyzing the LAI distribution maps inverted from different growth stages, it was found that the LAI inversion values basically coincided with the measured LAI values. The above results show that the domestic high-resolution remote sensing image has certain application value in crop physiological parameter inversion, and provides some references for related researches in the future.
WeChat
WeChat